mvpa2.measures.baseΒΆ
Plumbing for measures: algorithms that quantify properties of datasets.
Besides the Measure base class this module also provides the
(abstract) FeaturewiseMeasure class. The difference between a general
measure and the output of the FeaturewiseMeasure is that the latter
returns a 1d map (one value per feature in the dataset). In contrast there are
no restrictions on the returned value of Measure except for that it
has to be in some iterable container.
Functions
Classes
BinaryClassifierSensitivityAnalyzer(*args_, ...) |
Set sensitivity analyzer output to have proper labels |
BoostedClassifierSensitivityAnalyzer(*args_, ...) |
Set sensitivity analyzers to be merged into a single output |
CombinedFeaturewiseMeasure([analyzers, sa_attr]) |
Set sensitivity analyzers to be merged into a single output |
CrossValidation(learner, generator[, ...]) |
Cross-validate a learner’s transfer on datasets. |
FeatureSelectionClassifierSensitivityAnalyzer(...) |
Notes |
FeaturewiseMeasure([null_dist]) |
A per-feature-measure computed from a Dataset (base class). |
MappedClassifierSensitivityAnalyzer(*args_, ...) |
Set sensitivity analyzer output be reverse mapped using mapper of the |
Measure([null_dist]) |
A measure computed from a Dataset |
ProxyClassifierSensitivityAnalyzer(*args_, ...) |
Set sensitivity analyzer output just to pass through |
ProxyMeasure(measure[, skip_train]) |
Wrapper to allow for alternative post-processing of a shared measure. |
RegressionAsClassifierSensitivityAnalyzer(...) |
Set sensitivity analyzer output to have proper labels |
RepeatedMeasure(node, generator[, callback, ...]) |
Repeatedly run a measure on generated dataset. |
Sensitivity(clf[, force_train]) |
Sensitivities of features for a given Classifier. |
StaticMeasure([measure, bias]) |
A static (assigned) sensitivity measure. |
TransferMeasure(measure, splitter, **kwargs) |
Train and run a measure on two different parts of a dataset. |
